Intelligent interaction processing method and apparatus, device and computer storage medium

ABSTRACT

The present disclosure provides an intelligent interaction processing method and apparatus, a device and a computer storage medium. The method comprises: performing intention recognition for a preceding feedback item already returned to the user; continuing to return a subsequent feedback item to the user based on the intention of the preceding feedback item. According to the present disclosure, it is possible to guess the user&#39;s subsequent intention based on the preceding feedback item, and continue to return the desired subsequent feedback item to the user without the user&#39;s operations, so that the present disclosure is more intelligentized and richer and simplifies the user&#39;s operations.

The present application claims the priority of Chinese PatentApplication No. 201711138632.4, filed on Nov. 16, 2017, with the titleof “Intelligent interaction processing method and apparatus, device andcomputer storage medium”. The disclosure of the above applications isincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to the technical field of computerapplication, and particularly to an intelligent interaction processingmethod and apparatus, a device and a computer storage medium.

BACKGROUND OF THE DISCLOSURE

As mobile network and cloud computing develop rapidly, smart assistanttype applications particularly speech assistants have already beenapplied to diverse user equipment such as mobile phones, smart TV setsand smart loudspeaker boxes. They, through intelligent dialogue andintelligent interaction of instant question and answer, return the uservarious information that the user wants to know, even set applicationsand control other user equipment.

After obtaining the user's query, a conventional smart assistant returnsa single feedback item to the user. For example, when the user inputs“when is the latest match of Manchester United”, the smart assistantreturns “the latest match of Manchester United is at 05:00 Nov. 2, 2017,and the opponent is Arsenal”. If the user wants to operate subsequentlyor obtain subsequent information, he needs to further input a query.Again for example, when the user inputs the query “good morning”, thefeedback item returned by the smart assistant is information abouttoday's weather. When the user usually continues to want to knowinformation about today's road conditions, he needs to continue to inputthe query “what about today's road conditions”. Obviously,intelligentization and richness is insufficient, and operations arecomplicated.

SUMMARY OF THE DISCLOSURE

In view of the above, the present disclosure provides an intelligentinteraction processing method and apparatus, a device and a computerstorage medium, to improve intelligentization degree and richness ofintelligent interaction and simplifies the user's operations.

Specific Technical Solutions are as Follows:

The present disclosure provides an intelligent interaction processingmethod, the method comprising:

performing intention recognition for a preceding feedback item alreadyreturned to the user;

continuing to return a subsequent feedback item to the user based on theintention of the preceding feedback item.

According to a specific implementation mode of the present disclosure,the returning a subsequent feedback item to the user based on theintention of the preceding feedback item comprises:

determining a type of the subsequent feedback item corresponding to theintention:

obtaining an entity of the subsequent feedback item;

using the entity of the subsequent feedback item and the type of thesubsequent feedback item to determine the subsequent feedback itemreturned to the user.

According to a specific implementation mode of the present disclosure,the performing intention recognition for a preceding feedback itemalready returned to the user comprises:

extracting a keyword from the preceding feedback item, and determiningan intention of the preceding feedback item according to the keyword;or,

performing semantic analysis for the preceding feedback item, anddetermining the intention of the preceding feedback item; or

matching the preceding feedback item with a preset template, anddetermining an intention corresponding to the matched template as theintention of the preceding feedback item; or

using a machine learning model obtained by pre-training to performintention analysis for the preceding feedback item to obtain anintention of the preceding feedback item.

According to a specific implementation mode of the present disclosure,the determining a type of the subsequent feedback item corresponding tothe intention comprises:

determining the type of the subsequent feedback item corresponding tothe recognized intention, according to a correspondence relationshipbetween the preset intention and the type of the subsequent feedbackitem.

According to a specific implementation mode of the present disclosure,the correspondence relationship further comprises: a type of precedingfeedback item;

the method further comprises: determining the type of the precedingfeedback item already returned to the user;

the determining a type of the subsequent feedback item corresponding tothe recognized intention, according to a correspondence relationshipbetween the preset intention and the type of the subsequent feedbackitem comprises:

determining the corresponding type of the subsequent feedback itemaccording to the correspondence relationship between the type of thepreceding feedback item and the intention query of the precedingfeedback item.

According to a specific implementation mode of the present disclosure,before determining the type of the subsequent feedback, itemcorresponding to the intention, the method further comprises:

judging whether an intention confidence of the preceding feedback itemsatisfies a preset confidence requirement, and if yes, continuing toexecute of the determination of the type of the subsequent feedback itemcorresponding to the intention.

According to a specific implementation mode of the present disclosure,the type of the subsequent feedback item corresponding to the recognizedintention is determined further based on the user's environmentinformation.

According to a specific implementation mode of the present disclosure,the user's environment information comprises at least one of systemtime, user equipment type and user equipment action.

According to a specific implementation mode of the present disclosure,the obtaining the entity of the subsequent feedback item comprises:

regarding an entity extracted from the preceding feedback item as anentity of the subsequent feedback item: or

obtaining an entity from user data or user environment information as anentity of the subsequent feedback item.

According to a specific implementation mode of the present disclosure,the using the entity of the subsequent feedback item and the type of thesubsequent feedback item to determine the subsequent feedback itemcomprises:

using the entity of the subsequent feedback item and the type of thesubsequent feedback item to configure a search item;

obtaining a vertical search result corresponding to the search item asthe subsequent feedback item.

According to a specific implementation mode of the present disclosure,the using the entity of the subsequent feedback item and the type of thesubsequent feedback item to configure a search item comprises:

determining a template corresponding to the type of the subsequentfeedback item;

filling the entity of the subsequent feedback item into the determinedtemplate to obtain the search item.

According to a specific implementation mode of the present disclosure,the using the entity of the subsequent feedback item and the type of thesubsequent feedback item to determine the subsequent feedback itemcomprises:

using the entity of the subsequent feedback item and the type of thesubsequent feedback item to generate a control instruction;

sending the control instruction to an application or user equipmentcorresponding to the type of the subsequent feedback item.

According to a specific implementation mode of the present disclosure,the returning the subsequent feedback item to the user comprises:

directly returning the determined subsequent feedback item to the user;or

determining whether to return the determined subsequent feedback item tothe user based on the user's feedback.

According to a specific implementation mode of the present disclosure,transition wording is returned between the preceding feedback itemalready returned to the user and the subsequent feedback item returnedto the user;

the transition wording comprises a general-purpose word or sentence,blank, symbol, shadow or audio.

According to a specific implementation mode of the present disclosure,the subsequent feedback item comprises text, audio, video, image, link,and a control event of an application or user equipment.

The present disclosure further provides an intelligent interactionprocessing apparatus, the apparatus comprising:

an intention recognizing unit configured to perform intentionrecognition for a preceding feedback item already returned to the user;

a subsequent feedback unit configured to return a subsequent feedbackitem to the user based on the intention of the preceding feedback item.

According to a specific implementation node of the present disclosure,the subsequent feedback unit specifically comprises:

a type determining unit configured to determine a type of the subsequentfeedback item corresponding to the intention, based on the intention ofthe preceding feedback item.

an entity obtaining unit configured to obtain an entity of thesubsequent feedback item;

a feedback item determining unit configured to use the entity of thesubsequent feedback item and the type of the subsequent feedback item todetermine the subsequent feedback item;

a feedback item returning unit configured to return the determinedsubsequent feedback item to the user.

According to a specific implementation mode of the present disclosure,the intention recognizing unit specifically performs:

extracting a keyword from the preceding feedback item, and determiningan intention of the preceding feedback item according to the keyword;or,

performing semantic analysis for the preceding feedback item, anddetermining the intention of the preceding feedback item; or

matching the preceding feedback item with a preset template, anddetermining an intention corresponding to the matched template as theintention of the preceding feedback item; or

using a machine learning model obtained by pre-training to performintention analysis for the preceding feedback item to obtain anintention of the preceding feedback item.

According to a specific implementation mode of the present disclosure,the type determining unit specifically performs:

determining the type of the subsequent feedback item corresponding tothe recognized intention, according to a correspondence relationshipbetween the preset intention and the type of the subsequent feedbackitem.

According to a specific implementation mode of the present disclosure,the type determining unit determines the type of the subsequent feedbackitem corresponding to the recognized intention further based on theuser's environment information.

According to a specific implementation mode of the present disclosure,the entity obtaining unit specifically performs:

regarding an entity extracted from the preceding feedback item as anentity of the subsequent feedback item; or

obtaining an entity from user data or user environment information as anentity of the subsequent feedback item.

According to a specific implementation mode of the present disclosure,the feedback item determining unit specifically performs:

using the entity of the subsequent feedback item and the type of thesubsequent feedback item to configure a search item; obtaining avertical search result corresponding to the search item as thesubsequent feedback item; or

using the entity of the subsequent feedback item and the type of thesubsequent feedback item to generate a control instruction; sending thecontrol instruction to an application or user equipment corresponding tothe type of the subsequent feedback item.

According to a specific implementation mode of the present disclosure,the subsequent feedback unit specifically performs:

directly returning the determined subsequent feedback item to the user;or

determining whether to return the determined subsequent feedback item tothe user based on the user's feedback.

According to a specific implementation mode of the present disclosure,the subsequent feedback unit feeds back transition wording between thepreceding feedback item already returned to the user and the subsequentfeedback item returned to the user;

the transition wording comprises a general-purpose word or sentence,blank, symbol, shadow or audio.

The present disclosure further provides a device, comprising:

one or more processors;

a storage for storing one or more programs,

when said one or more programs are executed by said one or moreprocessors, said one or more processors are enabled to implement themethod.

The present disclosure further provides a storage medium containingcomputer executable instructions, wherein the computer executableinstructions, when executed by a computer processor, implement the abovemethod.

As can be seen from the above technical solutions, according to thepresent disclosure, it is possible to continue to return the subsequentfeedback item to the user based on the intention of the precedingfeedback item, thereby guessing the user's subsequent intention, andcontinue to return the desired subsequent feedback item to the userwithout the user's operation, so that the present disclosure is moreintelligentized and richer and simplifies the user's operations.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart of a method according to an embodiment of thepresent disclosure.

FIG. 2 is a structural diagram of an apparatus according to anembodiment of the present disclosure.

FIG. 3 is a block diagram of a computer system/server 012 adapted toimplement an implementation mode of the present disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present disclosure will be described in detail in conjunction withfigures and specific embodiments to make objectives, technical solutionsand advantages of the present disclosure more apparent.

Terms used in embodiments of the present disclosure are only intended todescribe specific embodiments, not to limit the present disclosure.Singular forms “a”, “said” and “the” used in embodiments and claims ofthe present disclosure are also intended to include plural forms, unlessother senses are clearly defined in the context.

It should be appreciated that the term “and/or” used in the text is onlyan association relationship depicting associated objects and representsthat three relations might exist, for example, A and/or B may representsthree cases, namely, A exists individually, both A and B coexist, and Bexists individually. In addition, the symbol “/” in the text generallyindicates associated objects before and after the symbol are in an “or”relationship.

Depending on the context, the word “if” as used herein may be construedas “at the time when . . . ” or “when . . . ” or “responsive todetermining” or “responsive to detecting”. Similarly, depending on thecontext, phrases “if is determined” or “if . . . (stated condition orevent) is detected” may be construed as “when . . . is determined” or“responsive to determining” or “when . . . (stated condition or event)is detected” or “responsive to detecting (stated condition or event)”.

A kernel idea of the present disclosure lies in performing intentionrecognition for a preceding feedback item already returned to the user,and consecutively returning a subsequent feedback item to the user basedon the intention of the preceding feedback item, thereby guessing theuser's subsequent intention and continuing to return the user thedesired subsequent feedback item without the user's operation. Themethod according to the present disclosure will be described in detailbelow in conjunction with embodiments.

FIG. 1 is a flow chart of a method according to an embodiment of thepresent disclosure. As shown in FIG. 1, the method may mainly comprisethe following steps:

At 101, perform intention recognition for a preceding feedback itemalready returned to the user.

In this step, the preceding feedback item already returned to the usermay be the latest feedback item already returned to the user, or may beall feedback items already returned to the user based on the user'scertain query. In the former case, it is feasible to perform intentionrecognition for the latest feedback item already returned to the user.In the latter case, it is feasible to perform overall intentionrecognition for all feedback items already returned to the user.

For example, assuming the user inputs the query “

(Eason Chan)”, return the user a feedback item “Eason Chan encyclopedia”based on the query, and it is possible to execute the process of thepresent disclosure by regarding

Eason Chan's encyclopedia

as a preceding feedback item, and determine a subsequent feedback item

Eason Chan's music list

. In next step, it is feasible to execute the process of the presentdisclosure by regarding

Eason Chan's music list

as the preceding feedback item and continue to determine a subsequentfeedback item, or it is also feasible to execute the process of thepresent disclosure by regarding

Eason Chan's encyclopedia

and

Eason Chan's music list

jointly as the preceding feedback item and continue to determine asubsequent feedback item.

Specifically, the following manners may be employed upon performingintention recognition for the preceding feedback item. At present, therealready exist relatively mature intention recognition manners. Severalimplementation modes are listed below, this is not limited to at leastone of the following several implementation modes:

The first manner: extracting a keyword from the preceding feedback item,and determining an intention of the preceding feedback item according tothe keyword.

In this manner: it is possible to preset keywords corresponding tovarious intention types, for example, “obtain a match result”corresponding to the keyword “match”. It is possible, after obtainingthe keyword from the preceding feedback item, determine the intention ofthe preceding feedback item based on the keyword, wherein one intentiontype may correspond to one or more keywords.

The second manner: performing semantic analysis for the precedingfeedback item, and determining the intention of the preceding feedbackitem.

It is feasible to predefine a correspondence relationship betweensemantics and intention, and after performing semantic analysis for thepreceding feedback item, determine an intention corresponding to thesemantics as the intention of the preceding feedback item. The presentembodiment of the present disclosure does not limit a specific manner ofperforming semantic analysis.

The third manner: matching the preceding feedback item with a presettemplate, and determining an intention corresponding to the matchedtemplate as the intention of the preceding feedback item.

It is feasible to preset templates corresponding to various intention,for example, an intention for setting the template “______ match startsat ______” is “setting a reminder”. If the preceding Feedback item is“the match between Manchester United and Arsenal starts at 20:00 Nov.20, 2017”, the preceding feedback item is matched with the abovetemplate and its intention is determined as “setting a reminder”.

The fourth manner: using a machine learning model obtained bypre-training, and performing intention analysis for the precedingfeedback item to obtain an intention of the preceding feedback item.

It is possible to pre-train the machine learning model by regardingfeedback items whose intentions are annotated, as training samples. Itis feasible to use the machine learning model obtained from training toperform intention analysis for the preceding feedback item to obtain anintention of the preceding feedback item.

At 102, determine a type of a subsequent feedback item corresponding tothe intention.

In this step, it is possible to preset subsequent feedback item typescorresponding to respective intentions. For example, a subsequentfeedback type corresponding to the intention

obtain a match result

is

a vertical search result of the match result

or

news of the match result

. Again for example, a subsequent feedback item type corresponding to

set a reminder

is

control an alarm clock event

.

The correspondence relationship between the above intentions andsubsequent feedback item types may be preset manually, or preset in themanner of machine learning.

In addition, in addition to depending on the intention of the precedingfeedback item, the type of the subsequent feedback item may further bedetermined in conjunction with the type of the preceding feedback itemand/or an intention confidence of the preceding feedback item.

For example, the pre-established correspondence relationship may be, thetype of the preceding feedback item, and the type of the subsequentfeedback item corresponding to the intention. That is to say, the typeof the corresponding subsequent feedback item can be determined onlywhen the type and intention of the preceding feedback item are hitsimultaneously.

Again for example, the type of the corresponding subsequent feedbackitem can be determined only when the intention of the preceding feedbackitem needs to meet a certain confidence requirement. If the confidenceis lower, it is impossible to continue to return the subsequent feedbackitem, namely, it is unnecessary to determine the subsequent feedbackitem.

In addition, the type of the subsequent feedback item may be further bedetermined based on the user's environment information.

The environment information may include at least one of system time,user equipment type and user equipment action. For example, if thepreceding feedback item is “the match between Manchester United andArsenal starts at 20:00 Nov. 20, 2017”, when the system time is alreadylater than the match time 20:00 Nov. 20, 2017, the subsequent feedbackitem should not be

set a reminded

. Again for example, if the preceding feedback item is “

(Xiaobao Song)” and if the current equipment type is a device having, adisplay screen such as a smart TV set or mobile phone, it is possible todetermine the corresponding subsequent feedback item is

Xiaobao Song's short sketch video

. Again for example, if the use equipment action is startup of a motorvehicle, it is possible to return the user relevant feedback, items suchas road condition information, weather information, traffic restrictioninformation and incompliance broadcast.

At 103, obtain an entity of the subsequent feedback item.

Manners of obtaining the entity of the subsequent feedback itemaccording to the present step may employ but not limited to thefollowing two manners:

The first manner: bring an entity from the preceding feedback item,namely, regard an entity extracted from the preceding feedback item asan entity of the subsequent feedback item. For example, if the precedingfeedback item is “today's weather”, the entity “today” therein may beregarded as the entity of the subsequent feedback item. Again forexample, if the preceding feedback item is “the match between ManchesterUnited and Arsenal starts at 20:00 Nov. 20, 2017”, the entity “20:00Nov. 20, 2017” therein may be regarded as the entity of the subsequentfeedback item. In addition, in some cases, it is possible to performconcatenation processing for the entity extracted from the precedingfeedback item, as the entity of the subsequent feedback item.

The second manner: bring an entity from the user data or userenvironment information, namely, obtain an entity from the user data oruser environment information as the entity of the subsequent feedbackitem. For example, the subsequent feedback item is determined as

road condition information

, it is possible to extract, from the user data, addresses of the user'shome and company to return the road condition information, or obtain theuser's current positioned address from the user environment information,to return the road condition information.

At 104, use the entity of the subsequent feedback item and the type ofthe subsequent feedback item to determine the subsequent feedback item.

Manners of determining the subsequent feedback item in the present stepmay include but not limited to the following two manners:

The first manner: use the entity of the subsequent feedback item and thetype of the subsequent feedback item to configure a search item; obtaina vertical search result corresponding to the search item as thesubsequent feedback item.

Upon configuring the search item, it is possible to determine a templatecorresponding to the type of the subsequent feedback item, and fill theentity of the subsequent feedback item into the determined template toobtain the search item.

For example, if the preceding feedback item is “today's weather”, theentity “today” may be filled into the template “traffic restricted______” to obtain the search item “traffic restricted today”. Then, itis possible to use the search item “traffic restricted today” to performvertical searching to obtain that the search result of “trafficrestricted today” is “vehicles with tail digits 2 and 7 restrictedtoday”, and regard it as the subsequent feedback item.

Again for example, if the preceding feedback item is “Eason Chanencyclopedia”, the entity “Eason Chan” may be filled into the template“______song” to obtain the search item “Eason Chan song”. Then, it isfeasible to use the search item “Eason Chan song” to perform verticalsearch, and obtain a search result of “Eason Chan song” is a link listof Eason Chan's songs, or is audio resources of Eason Chan's songs.

The second manner: use the entity of the subsequent feedback item andthe type of the subsequent feedback item to generate a controlinstruction; send the control instruction to an application or userequipment corresponding to the type of the subsequent feedback item.

For example, if the preceding feedback item is “the match betweenManchester United and Arsenal starts at 20:00 Nov. 20, 2017”, it ispossible to combine the entity “20:00 Nov. 20, 2017” therein with “set areminder” to generate a control instruction “set a reminder of 20:00Nov. 20, 2017”, and then send it to an alarm clock or reminder-typeapplication, or to an alarm clock device.

At 105, return the determined subsequent feedback item to the user.

The determined subsequent feedback item may be returned directly to theuser, or whether to return to the user may be determined based on theuser's feedback. For example, assuming the subsequent feedback item “seta reminder of 20:00 Nov. 20, 2017” is generated, the user may beprompted of whether to set the reminder. If the user chooses to set, areminder is set, and successful setting of the reminder is returned.Again for example, assuming that the subsequent feedback item “EasonChan's song resources” is generated, the user is prompted of whether toplay the song resources. If the user chooses to play, Eason Chan's songresources are played to the user.

The subsequent feedback item involved in the embodiment of the presentdisclosure may include but not limited to text, audio, video, image,link, and a control event of an application or user equipment.

In addition, there may exist transition wording between the precedingfeedback item and the subsequent feedback item returned to the user. Thetransition wording may act as a transition between two consecutivefeedback items. The transition wording may be some universal words suchas “next”, “then” and “next feedback item”.

The transition wording may also be blank, i.e., it is blank between thepreceding feedback item and the subsequent feedback item.

The transition wording may also be some symbols. This case is welladapted for devices having a screen such as a mobile phone and smart TVset, for example. “

” may be employed between the two consecutive feedback items forseparation.

The transition wording may also be some shadow. This case is adapted forany type of user equipment. Certain shadow is generated through a lightsource such as an LED on the device. For example, the LED may be usedbetween the two consecutive feedback items to generate a light ring of acertain color, to separate the two consecutive feedback items.

The transition wording may also be some audio. This case is adapted fora device having a loudspeaker such as a mobile phone, a smartloudspeaker box, and a smart TV set. For example, a short audio such as“tinkle” may be employed between the two consecutive feedback items forseparation.

The above describes the method according to the present disclosure indetail. A subject for performing the above method may be a processingdevice for intelligent interaction. The device may be located at anapplication of the user equipment, or may be a function unit such as aplug-in or Software Development Kit (SDK) in the application located inthe user equipment. This is not particularly limited in the embodimentof the present disclosure. An apparatus according to the presentdisclosure will be described in detail in conjunction with anembodiment.

FIG. 2 is a structural diagram of an apparatus according to anembodiment of the present disclosure. The apparatus may be disposed in asmart assistant-type application. As shown in FIG. 2, the apparatus maycomprise: an intention recognizing unit 00 and a subsequent feedbackunit 10. Main functions of the units are as follows:

The intention recognizing unit 00 is configured to perform intentionrecognition for a preceding feedback item already returned to the user.

The preceding feedback item already returned to the user may be thelatest feedback item already returned to the user, or may be allfeedback items already returned to the user based on the user's certainquery. In the former case, it is feasible to perform intentionrecognition for the latest feedback item already returned to the user.In the latter case, it is feasible to perform overall intentionrecognition for all feedback items already returned to the user.

Specifically, the intention recognizing unit 00 may perform intentionrecognition for the preceding feedback item already returned to the userin the following manners, but not limited to the following manners:

The first manner: extracting a keyword from the preceding feedback item,and determining an intention of the preceding feedback item according tothe keyword.

In this manner, it is possible to preset keywords corresponding tovarious intention types. It is possible, after obtaining the keywordfrom the preceding feedback item, determine the intention of thepreceding feedback item based on the keyword, wherein one intention typemay correspond to one or more keywords.

The second manner: performing semantic analysis for the precedingfeedback item, and determining the intention of the preceding feedbackitem.

It is feasible to predefine a correspondence relationship betweensemantics and intention, and after performing semantic analysis for thepreceding feedback item, determine an intention corresponding to thesemantics as the intention of the preceding feedback item. The presentembodiment of the present disclosure does not limit a specific manner ofperforming semantic analysis.

The third manner: matching the preceding feedback item with a presettemplate, and determining an intention corresponding to the matchedtemplate as the intention of the preceding feedback item.

The fourth manner: using a machine learning model obtained bypre-training to perform intention analysis for the preceding feedbackitem to obtain an intention of the preceding feedback item.

It is possible to pre-train the machine learning model by regardingfeedback items whose intentions are annotated, as training samples. Itis feasible to use the machine learning model obtained from training toperform intention analysis for the preceding feedback item to obtain anintention of the preceding feedback item.

The subsequent feedback unit 10 is configured to return the subsequentfeedback item to the user based on the intention of the precedingfeedback item.

Specifically, the subsequent feedback unit 10 may comprise a typedetermining unit 11, an entity obtaining unit 12, a feedback itemdetermining unit 13 and a feedback item returning unit 14.

The type determining unit 11 is configured to determine a subsequentfeedback item type corresponding to the intention, based on theintention of the preceding feedback item.

Specifically, the type determining unit 11 may determine a subsequentfeedback item type corresponding to the recognized intention, accordingto a correspondence relationship between the preset intention and thetype of the subsequent feedback item.

The correspondence relationship between the above intentions andsubsequent feedback item types may be preset manually, or preset in themanner of machine learning.

In addition, the type determining unit 11 may determine the type of thesubsequent feedback item, in addition to depending on the intention ofthe preceding feedback item, in conjunction with the type of thepreceding feedback item and/or an intention confidence of the precedingfeedback item.

For example, the pre-established correspondence relationship may be; thetype of the preceding feedback item, and the type of the subsequentfeedback item corresponding to the intention. That is to say, the typeof the corresponding subsequent feedback item can be determined onlywhen the type and intention of the preceding feedback item are hitsimultaneously.

Again for example, the type of the corresponding subsequent feedbackitem can be determined only when the intention of the preceding feedbackitem needs to meet a certain confidence requirement. If the confidenceis lower, it is impossible to continue to return the subsequent feedbackitem, namely, it is unnecessary to determine the subsequent feedbackitem.

In addition, the type determining unit 11 may determine the type of thesubsequent feedback item further based on the user's environmentinformation. The environment information may include at least one ofsystem time, user equipment type and user equipment action.

The entity obtaining unit 12 is configured to obtain an entity of thesubsequent feedback item.

Specifically, the entity obtaining unit 12 may regard an entityextracted from the preceding feedback item as an entity of thesubsequent feedback item; or obtain an entity from user data or userenvironment information as an entity of the subsequent feedback item.

The feedback item determining unit 13 is configured to use the entity ofthe subsequent feedback item and the type of the subsequent feedbackitem to determine the subsequent feedback item.

Manners in which the feedback item determining unit 13 determining thesubsequent feedback item may include but not limited to the followingtwo manners:

the first manner: use the entity of the subsequent feedback item and thetype of the subsequent feedback item to configure a search item: obtaina vertical search result corresponding to the search item as thesubsequent feedback item.

Upon configuring the search item, it is possible to determine a templatecorresponding to the type of the subsequent feedback item, and fill theentity of the subsequent feedback item into the determined template toobtain the search item.

The second manner: use the entity of the subsequent feedback item andthe type of the subsequent feedback item to generate a controlinstruction; send the control instruction to an application or userequipment corresponding to the type of the subsequent feedback item.

The feedback item returning unit 14 is configured to return thedetermined subsequent feedback item to the user.

The feedback item returning unit 14 may directly return the determinedsubsequent feedback item to the user, or determine whether to return thedetermined subsequent feedback item to the user based on the user'sfeedback.

The subsequent feedback item involved in the embodiment of the presentdisclosure may include but not limited to text, audio, video, image,link, and a control event of an application or user equipment.

In addition, the feedback item returning unit 14 may feed backtransition wording between the preceding feedback item already returnedto the user and the subsequent feedback item returned to the user.

The transition wording may be some universal words such as “next”,“then” and “next feedback item”.

The transition wording may also be blank, i.e., it is blank between thepreceding feedback item and the subsequent feedback item.

The transition wording may also be some symbols. This case is welladapted for devices having a screen such as a mobile phone and smart TVset, for example. “

” may be employed between the two consecutive feedback items forseparation.

The transition wording may also be some shadow. This case is adapted forany type of user equipment. Certain shadow is generated through a lightsource such as an LED on the device. For example, the LED may be usedbetween the two consecutive feedback items to generate a light ring of acertain color, to separate the two consecutive feedback items.

The transition wording may also be some audio. This case is adapted fora device having a loudspeaker such as a mobile phone, a smartloudspeaker box, and a smart TV set. For example, a short audio such as“tinkle” may be employed between the two consecutive feedback items forseparation.

Two examples are provided below:

When the user inputs the query “when is the latest match of ManchesterUnited”, the smart assistant returns the feedback item “the latest matchof Manchester United is at 05:00 Nov. 2, 2017, and the opponent isArsenal”, and this feedback item is regarded as the preceding feedbackitem. Then, the manner according to the present disclosure is employedto determine that the subsequent feedback item is

set a reminder of the match at 05:00 Nov. 2, 2017

, and the smart assistant may return to the user whether to set a promptinformation of the reminder. If the user chooses to set the promptinformation, a control instruction

set a reminder of the match at 05:00 Nov. 2, 2017

is generated for a smart alarm clock, thereby creating the reminder. Theuser needn't manually set the reminder. The present disclosure isobviously more intelligentized and simplifies the user operations.

When the user inputs the query “good morning” via a vehicle-mountedintelligent assistant, the feedback item returned by the smart assistantis

today's weather

, namely, information about the today's. weather.

today's weather

is regarded as the preceding feedback item, the subsequent feedback item

today's road conditions

is determined in the manner provided by the present disclosure, and itis possible to automatically obtain that the current time is the timewhen the user goes to work, and automatically search to obtaininformation about road conditions from the user's home to the user'scompany. The user needn't input the query for today's road conditionsand manually set a departure and a destination. The present disclosureis obviously more intelligentized and simplifies the user operation.

When the user inputs “Eason Chan” via the mobile phone, the feedbackitem returned by the smart assistant is “Eason Chan encyclopedia”. It ispossible to regard the feedback item as the preceding feedback item,determine the subsequent feedback item

Eason Chan's song

in the manner provided by the present disclosure, and automaticallysearch to obtain and return Eason Chan's hot song to the user. The usermay directly choose to listen to the song, without manually searchingfor Eason Chan's songs, which is obviously more intelligentized andricher and simplifies the user's operations.

FIG. 3 illustrates a block diagram of an example computer system/server012 adapted to implement an implementation mode of the presentdisclosure. The computer system/server 012 shown in FIG. 3 is only anexample and should not bring about any limitation to the function andscope of use of the embodiments of the present disclosure.

As shown in FIG. 3, the computer system/server 012 is shown in the formof a general-purpose computing device. The components of computersystem/server 012 may include, but are not limited to, one or moreprocessors or processing units 016, a memory 028, and a bus 018 thatcouples various system components including system memory 028 and theprocessor 016.

Bus 018 represents one or more of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 012 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 012, and it includes both volatileand non-volatile media, removable and non-removable media.

Memory 028 can include computer system readable media in the form ofvolatile memory, such as random access memory (RAM) 030 and/or cachememory 032, Computer system/server 012 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 034 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown in FIG. 3 and typically called a “hard drive”). Although notshown in FIG. 3, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each drive can be connected tobus 018 by one or more data media interfaces. The memory 028 may includeat least one program product having a set (e.g., at least one) ofprogram modules that are configured to carry out the functions ofembodiments of the present disclosure.

Program/utility 040, having a set (at least one) of program modules 042,may be stored in the system memory 028 by way of example, and notlimitation, as well as an operating system, one or more disclosureprograms, other program modules, and program data. Each of theseexamples or a certain combination thereof might include animplementation of a networking environment. Program modules 042generally carry out the functions and/or methodologies of embodiments ofthe present disclosure.

Computer system/server 012 may also communicate with one or moreexternal devices 014 such as a keyboard, a pointing device, a display024, etc.; with one or more devices that enable a user to interact withcomputer system/server 012; and/or with any devices (e.g., network card,modem, etc.) that enable computer system/server 012 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 022. Still yet, computer system/server 012can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 020. As depicted in FIG. 3, networkadapter 020 communicates with the other communication modules ofcomputer system/server 012 via bus 018. It should be understood thatalthough not shown, other hardware and/or software modules could be usedin conjunction with computer system/server 012. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc.

The processing unit 016 executes various function applications and dataprocessing by running programs stored in the memory 028, for example,implement the steps of the method according to embodiments of thepresent disclosure.

The aforesaid computer program may be arranged in the computer storagemedium, namely, the computer storage medium is encoded with the computerprogram. The computer program, when executed by one or more computers,enables one or more computers to execute the flow of the method and/oroperations of the apparatus as shown in the above embodiments of thepresent disclosure. For example, the steps of the method according toembodiments of the present disclosure are performed by the one or moreprocessors.

As time goes by and technologies develop, the meaning of medium isincreasingly broad. A propagation channel of the computer program is nolonger limited to tangible medium, and it may also be directlydownloaded from the network. The computer-readable medium of the presentembodiment may employ any combinations of one or more computer-readablemedia. The machine readable medium may be a machine readable signalmedium or a machine readable storage medium. A machine readable mediummay include, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice, or any suitable combination of the foregoing. More specificexamples of the machine readable storage medium would include anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), a portable compact disc read-only memory (CD-ROM), an opticalstorage device, a magnetic storage device, or any suitable combinationof the foregoing. In the text herein, the computer readable storagemedium can be any tangible medium that include or store programs for useby an instruction execution system, apparatus or device or a combinationthereof.

The computer-readable signal medium may be included in a baseband orserve as a data signal propagated by part of a carrier, and it carries acomputer-readable program code therein. Such propagated data signal maytake many forms, including, but not limited to, electromagnetic signal,optical signal or any suitable combinations thereof. Thecomputer-readable signal medium may further be any computer-readablemedium besides the computer-readable storage medium, and thecomputer-readable medium may send, propagate or transmit a program foruse by an instruction execution system, apparatus or device or acombination thereof.

The program codes included by the computer-readable medium may betransmitted with any suitable medium, including, but not limited toradio, electric wire, optical cable, RF or the like, or any suitablecombination thereof.

Computer program code for carrying out operations disclosed herein maybe written in one or more programming languages or any combinationthereof. These programming languages include an object orientedprogramming language such as Java, Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

In the embodiments provided by the present disclosure, it should beunderstood that the revealed system, apparatus and method can beimplemented in other ways. For example, the above-described embodimentsfor the apparatus are only exemplary, e.g., the division of the units ismerely logical one, and, in reality, they can be divided in other waysupon implementation.

What are stated above are only preferred embodiments of the presentdisclosure and not intended to limit the present disclosure. Anymodifications, equivalent substitutions and improvements made within thespirit and principle of the present disclosure all should be included inthe extent of protection of the present disclosure.

What is claimed is:
 1. A computer-implemented intelligent interactionprocessing method, wherein the method comprises: returning a precedingfeedback item to a user based on a query; performing intentionrecognition for the preceding feedback item already returned to theuser; continuing to return a subsequent feedback item to the user basedon the intention of the preceding feedback item, the subsequent feedbackitem being independent of the query based on which the precedingfeedback item returned to the user is generated, and the subsequentfeedback item being independent of a follow-up or subsequent query,wherein the subsequent feedback item is returned without receiving asubsequent query, wherein the returning a subsequent feedback item tothe user based on the intention of the preceding feedback itemcomprises: determining a type of the subsequent feedback itemcorresponding to the intention; obtaining an entity of the subsequentfeedback item; using the entity of the subsequent feedback item and thetype of the subsequent feedback item to determine the subsequentfeedback item returned to the user, wherein the determining a type ofthe subsequent feedback item corresponding to the intention comprises:determining the type of the subsequent feedback item corresponding tothe recognized intention, according to the preset correspondencerelationship between the intentions and the type of the subsequentfeedback item, the subsequent feedback item comprising text, audio,video, image, link, and a control event of an application or userequipment.
 2. The method according to claim 1, wherein the performingintention recognition for a preceding feedback item already returned tothe user comprises: extracting a keyword from the preceding feedbackitem, and determining an intention of the preceding feedback itemaccording to the keyword; or, performing semantic analysis for thepreceding feedback item, and determining the intention of the precedingfeedback item; or matching the preceding feedback item with a presettemplate, and determining an intention corresponding to the matchedtemplate as the intention of the preceding feedback item; or using amachine learning model obtained by pre-training to perform intentionanalysis for the preceding feedback item to obtain an intention of thepreceding feedback item.
 3. The method according to claim 1, wherein thecorrespondence relationship further comprises: a type of the precedingfeedback item; the method further comprises: determining the type of thepreceding feedback item already returned to the user; the determining atype of a subsequent feedback item corresponding to the recognizedintention, according to a correspondence relationship between the presetintention and the type of the subsequent feedback item comprises:determining the type of the corresponding subsequent feedback itemaccording to the correspondence relationship between the type of thepreceding feedback item and an intention query of the preceding feedbackitem.
 4. The method according to claim 1, wherein before determiningtype of the subsequent feedback item corresponding to the intention, themethod further comprises: judging whether an intention confidence of thepreceding feedback item satisfies a preset confidence requirement, andif yes, continuing to execute of the determination of the type of thesubsequent feedback item corresponding to the intention.
 5. The methodaccording to claim 1, wherein the type of the subsequent feedback itemcorresponding to the recognized intention is determined further based onthe user's environment information.
 6. The method according to claim 5,wherein the user's environment information comprises at least one ofsystem time, user equipment type and user equipment action.
 7. Themethod according to claim 1, wherein the obtaining the entity of thesubsequent feedback item comprises: regarding an entity extracted fromthe preceding feedback item as an entity of the subsequent feedbackitem; or obtaining an entity from user data or user environmentinformation as an entity of the subsequent feedback item.
 8. The methodaccording to claim 1, wherein the using the entity of the subsequentfeedback item and the type of the subsequent feedback item to determinethe subsequent feedback item comprises: using the entity of thesubsequent feedback item and the type of the subsequent feedback item toconfigure a search item; obtaining a vertical search resultcorresponding to the search item as the subsequent feedback item.
 9. Themethod according to claim 8, wherein the using the entity of thesubsequent feedback item and the type of the subsequent feedback item toconfigure a search item comprises: determining a template correspondingto the type of the subsequent feedback item; filling the entity of thesubsequent feedback item into the determined template to obtain thesearch item.
 10. The method according to claim 1, wherein the using theentity of the subsequent feedback item and the type of the subsequentfeedback item to determine the subsequent feedback item comprises: usingthe entity of the subsequent feedback item and the type of thesubsequent feedback item to generate a control instruction; sending thecontrol instruction to an application or user equipment corresponding tothe type of the subsequent feedback item.
 11. The method according toclaim 1, wherein the returning the subsequent feedback item to the usercomprises: directly returning the determined subsequent feedback item tothe user; or determining whether to return the determined subsequentfeedback item to the user based on the user's feedback.
 12. The methodaccording to claim 1, wherein transition wording is returned between thepreceding feedback item already returned to the user and the subsequentfeedback item returned to the user; the transition wording comprises ageneral-purpose word or sentence, blank, symbol, shadow or audio. 13.The method according to claim 1, wherein the subsequent feedback itemcomprises text, audio, video, image, link, and a control event of anapplication or user equipment.
 14. A device, wherein the devicecomprises: one or more processors; a storage for storing one or moreprograms, when said one or more programs are executed by said one ormore processors, said one or more processors are enabled to implement anintelligent interaction processing method, wherein the method comprises:returning a preceding feedback item to a user based on a query;performing intention recognition for the preceding feedback item alreadyreturned to the user; continuing to return a subsequent feedback item tothe user based on the intention of the preceding feedback item, thesubsequent feedback item being independent of the query based on whichthe preceding feedback item returned to the user is generated, and thesubsequent feedback item being independent of a follow-up or subsequentquery, wherein the subsequent feedback item is returned withoutreceiving a subsequent query, wherein the returning a subsequentfeedback item to the user based on the intention of the precedingfeedback item comprises: determining a type of the subsequent feedbackitem corresponding to the intention; obtaining an entity of thesubsequent feedback item; using the entity of the subsequent feedbackitem and the type of the subsequent feedback item to determine thesubsequent feedback item returned to the user, wherein the determining atype of the subsequent feedback item corresponding to the intentioncomprises: determining the type of the subsequent feedback itemcorresponding to the recognized intention, according to the presetcorrespondence relationship between the intentions and the type of thesubsequent feedback item, the subsequent feedback item comprising text,audio, video, image, link, and a control event of an application or userequipment.
 15. The device according to claim 14, wherein the performingintention recognition for a preceding feedback item already returned tothe user comprises: extracting a keyword from the preceding feedbackitem, and determining an intention of the preceding feedback itemaccording to the keyword; or, performing semantic analysis for thepreceding feedback item, and determining the intention of the precedingfeedback item; or matching the preceding feedback item with a presettemplate, and determining an intention corresponding to the matchedtemplate as the intention of the preceding feedback item; or using amachine learning model obtained by pre-training to perform intentionanalysis for the preceding feedback item to obtain an intention of thepreceding feedback item.
 16. A non-transitory storage medium containingcomputer executable instructions, wherein the computer executableinstructions, when executed by a computer processor, implement anintelligent interaction processing method, wherein the method comprises:returning a preceding feedback item to a user based on a query;performing intention recognition for the preceding feedback item alreadyreturned to the user; continuing to return a subsequent feedback item tothe user based on the intention of the preceding feedback item, thesubsequent feedback item being independent of the query based on whichthe preceding feedback item returned to the user is generated, and thesubsequent feedback item being independent of a follow-up or subsequentquery, wherein the subsequent feedback item is returned withoutreceiving a subsequent query, wherein the returning a subsequentfeedback item to the user based on the intention of the precedingfeedback item comprises: determining a type of the subsequent feedbackitem corresponding to the intention; obtaining an entity of thesubsequent feedback item; using the entity of the subsequent feedbackitem and the type of the subsequent feedback item to determine thesubsequent feedback item returned to the user, wherein the determining atype of the subsequent feedback item corresponding to the intentioncomprises: determining the type of the subsequent feedback itemcorresponding to the recognized intention, according to the presetcorrespondence relationship between the intentions and the type of thesubsequent feedback item, the subsequent feedback item comprising text,audio, video, image, link, and a control event of an application or userequipment.